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Machine Learning Techniques for Software Quality Assurance: A Survey
[article]
2021
arXiv
pre-print
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In this survey, we discuss various approaches in both fault prediction and test case prioritization, also explaining how in recent studies deep learning algorithms for fault prediction help to bridge the ...
Closely related to estimating defect-prone parts of a software system is the question of how to select and prioritize test cases, and indeed test case prioritization has been extensively researched as ...
Deep learning-based approaches require only the source code of the training and test projects, and are therefore suitable for both within-project and cross-project defect prediction. ...
arXiv:2104.14056v1
fatcat:b6hch7gimbefhpz6n5552waq5i
Software Vulnerability Analysis and Discovery using Deep Learning Techniques: A Survey
2020
IEEE Access
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For this purpose, combined with multiple heterogeneous vulnerabilityrelated data sources, a deep learning framework based on the LSTM unit learns the unified vulnerability source code. ...
APPLICATION SCOPE In the field of software vulnerability detection based on deep learning, most works surveyed in this paper detect vulnerabilities in source code, such as [31] , [32] , [35] , [37] ...
doi:10.1109/access.2020.3034766
fatcat:3fpbunyedza2ree3ozle6o63ce
A Survey of Automatic Software Vulnerability Detection, Program Repair, and Defect Prediction Techniques
2020
Security and Communication Networks
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The development of deep learning technology has brought new opportunities for the study of potential security issues in software, and researchers have successively proposed many automation methods. ...
At the same time, we point out some problems of these research methods, give corresponding solutions, and finally look forward to the application prospect of deep learning technology in automated software ...
Deep learning-based static vulnerability detection methods analyse program code dependencies on the source code level and preprocess the vulnerable code based on the idea of program slicing. ...
doi:10.1155/2020/8858010
fatcat:obeiw4p7afan5m24ydmdkmyhbm
Finding Reusable Machine Learning Components to Build Programming Language Processing Pipelines
[article]
2022
arXiv
pre-print
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To improve the findability, accessibility, interoperability and reusability (FAIRness) of machine learning components, we collect and analyze a set of representative papers in the domain of machine learning-based ...
Finally, we show some example use cases of leveraging the reusable components to construct machine learning pipelines to solve a set of PLP tasks. ...
A taxonomy is designed based on the underlying design principles of each model and used to survey the latest published literature. ...
arXiv:2208.05596v1
fatcat:4pp7zsxvynf35fraa2wz65epd4
Predictive Models in Software Engineering: Challenges and Opportunities
[article]
2020
arXiv
pre-print
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This paper is a first attempt to systematically organize knowledge in this area by surveying a body of 139 papers on predictive models. ...
Based on our findings, we also propose a set of current challenges that still need to be addressed in future work and provide a proposed research road map for these opportunities. ...
Deep learning is part of a broader family of machine learning methods based on artificial neural networks [121] . ...
arXiv:2008.03656v1
fatcat:fe7ylphujfbobeo3g5yevniiei
The Coming Era of AlphaHacking? A Survey of Automatic Software Vulnerability Detection, Exploitation and Patching Techniques
[article]
2018
arXiv
pre-print
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In this paper, we give an extensive survey of former representative works related to the underlying technologies of a CRS, including vulnerability detection, exploitation and patching. ...
is inseparable from machine learning. ...
Most machine learning algorithms are designed with source code while binary-based machine learning is rare. ...
arXiv:1805.11001v2
fatcat:uh5ndhgmt5gpdk4opritn5fnsq
Software defect prediction
2021
figshare.com
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Different methods have been developed to quickly predict the most likely locations of defects in large code bases. ...
Most of them focus on designing features (e.g. complexity metrics) that correlate with potentially defective code. ...
Most of the modern techniques for software defect prediction problem uses deep learning techniques. Deep learning is a machine learning technique based on artificial neural networks. ...
doi:10.6084/m9.figshare.14401304.v1
fatcat:2ngmsh337baf7n6xbvzybzaopu
An in-Depth Analysis of the Software Features' Impact on the Performance of Deep Learning-Based Software Defect Predictors
2022
IEEE Access
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In this paper, we are conducting an in-depth analysis on the software features' impact on the performance of deep learning-based software defect predictors. ...
A broad evaluation performed on the Calcite software system highlights a statistically significant improvement obtained by applying deep learning-based classifiers for detecting software defects when using ...
[11] ) introduced Deep Belief Neural Networks (DBN) for performing defect prediction based on code analysis. Wang et al. ...
doi:10.1109/access.2022.3181995
fatcat:hplspug54rbqflnqyfl4fbw3tu
A Survey on Machine Learning Techniques for Source Code Analysis
[article]
2021
arXiv
pre-print
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Objective: We aim to summarize the current knowledge in the area of applied machine learning for source code analysis. ...
Context: The advancements in machine learning techniques have encouraged researchers to apply these techniques to a myriad of software engineering tasks that use source code analysis such as testing and ...
A Survey on Machine Learning Techniques for Source Code Analysis • 0:5
https://clang.llvm.org/
ACM Trans. Softw. Eng. Methodol., Vol. 0, No. 0, Article 0. Publication date: 2021. ...
arXiv:2110.09610v1
fatcat:jc6c3jnxcbekfbssyy7hn3zwxa
Object oriented quality prediction through artificial intelligence and machine learning: a survey
2020
ACCENTS Transactions on Information Security
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Software quality estimation is an important aspect as it eliminates design and code defects. ...
In this paper a survey and the case analytics have been presented for the object-oriented quality prediction. It shows the analytical and experimental aspects of previous methodologies. ...
There is the need of decision support system for the computation of fault-based behaviors so that it can be recognized easily.
5.Conclusion In this paper a survey and analysis has been presented based ...
doi:10.19101/tis.2020.517005
fatcat:vqsixyz3nbco7m4i2tpjoymvgu
Data Mining and Machine Learning for Software Engineering
[chapter]
2020
Data Mining - Methods, Applications and Systems [Working Title]
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Various data mining and machine learning studies have been conducted to deal with software engineering tasks such as defect prediction, effort estimation, etc. ...
Software engineering is one of the most utilizable research areas for data mining. Developers have attempted to improve software quality by mining and analyzing software data. ...
different machine learning types: supervised and unsupervised learning, especially on ensemble learning and deep learning. ...
doi:10.5772/intechopen.91448
fatcat:t4sqbohfdzhrdnj643sbkasiqq
A Survey on Deep Learning for Software Engineering
[article]
2020
arXiv
pre-print
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Based on our findings, we present a set of current challenges remaining to be investigated and outline a proposed research road map highlighting key opportunities for future work. ...
To fill this gap, we performed a survey to analyse the relevant studies published since 2006. We first provide an example to illustrate how deep learning techniques are used in SE. ...
(2) We provide a classification of DL models used in SE based on their architectures and an analysis of DL technique selection strategy. (3) We performed a comprehensive analysis on the key factors ...
arXiv:2011.14597v1
fatcat:pcyg6zbnm5bc3g4yhjomcnye3y
Wireless Personal Communications: Machine Learning for Big Data Processing in Mobile Internet
2018
Wireless personal communications
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Based on these features, deep neural network (DNN) are trained to form a defect prediction model in order to achieve a high accuracy. ...
called ACVC (adaptive cooperative video coding) based on AJSCC and with the concept of coset coding in distributed source coding, to improve the overall video broadcast quality in wireless cooperative ...
doi:10.1007/s11277-018-5916-x
fatcat:r6ik6co7pjabhoc5jczn3ecy6e
Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges
2020
Materials
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The detection of product defects is essential in quality control in manufacturing. This study surveys stateoftheart deep-learning methods in defect detection. ...
Third, we summarize and analyze the application of ultrasonic testing, filtering, deep learning, machine vision, and other technologies used for defect detection, by focusing on three aspects, namely method ...
Table 4 Survey of Deep-learning methods in defect methods based on deep learning have achieved the best experimental result thus far. ...
doi:10.3390/ma13245755
pmid:33339413
pmcid:PMC7766692
fatcat:egyrdqjmqvebvoeaqf5yyi3cxm
Synergy between Machine/Deep Learning and Software Engineering: How Far Are We?
[article]
2020
arXiv
pre-print
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Since 2009, the deep learning revolution, which was triggered by the introduction of ImageNet, has stimulated the synergy between Machine Learning (ML)/Deep Learning (DL) and Software Engineering (SE). ...
Our trend analysis demonstrated the mutual impacts that ML/DL and SE have had on each other. ...
Most of the existing code summarization methods learn the semantic representation of source codes based on statistical language models. ...
arXiv:2008.05515v1
fatcat:bhngzoqabjeebpcuxuc4gfvfr4
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